abdo-Mansour commited on
Commit
4e62a86
·
1 Parent(s): 7a692a6

why aren't you working

Browse files
web2json/__pycache__/ai_extractor.cpython-311.pyc CHANGED
Binary files a/web2json/__pycache__/ai_extractor.cpython-311.pyc and b/web2json/__pycache__/ai_extractor.cpython-311.pyc differ
 
web2json/ai_extractor.py CHANGED
@@ -224,7 +224,7 @@ class NvidiaLLMClient(LLMClient):
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  """
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  self.model_name = model_name
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- @retry_on_ratelimit(max_retries=6, base_delay=0.5, max_delay=5.0)
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  def call_api(self, prompt: str) -> str:
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  """
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  Call the NVIDIA API with the given prompt (non-streaming).
@@ -241,7 +241,8 @@ class NvidiaLLMClient(LLMClient):
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  messages=[{"role": "user", "content": prompt}],
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  temperature=self.temperature,
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  top_p=self.top_p,
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- max_tokens=self.max_tokens
 
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  # stream is omitted (defaults to False)
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  )
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  # print("DONE")
@@ -262,6 +263,7 @@ class NvidiaLLMClient(LLMClient):
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  idx = futures[fut]
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  try:
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  results[idx] = fut.result()
 
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  except RateLimitError:
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  # You could set results[idx] = None or a default string
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  results[idx] = f"<failed after retries>"
@@ -367,6 +369,7 @@ class ModalRerankerClient(RerankerClient):
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  scores = np.array([p["score"] for p in ranked], dtype=float)
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  # Max normalization
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  max_score = scores.max() if len(scores) > 0 else 1.0
 
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  if max_score == 0:
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  norm_scores = np.zeros_like(scores)
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  else:
@@ -381,6 +384,7 @@ class ModalRerankerClient(RerankerClient):
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  for p, norm in filtered
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  ]
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  return docs
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  """
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  self.model_name = model_name
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+ @retry_on_ratelimit(max_retries=20, base_delay=0.5, max_delay=5.0)
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  def call_api(self, prompt: str) -> str:
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  """
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  Call the NVIDIA API with the given prompt (non-streaming).
 
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  messages=[{"role": "user", "content": prompt}],
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  temperature=self.temperature,
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  top_p=self.top_p,
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+ max_tokens=self.max_tokens,
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+ extra_body={"chat_template_kwargs": {"thinking":True}},
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  # stream is omitted (defaults to False)
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  )
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  # print("DONE")
 
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  idx = futures[fut]
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  try:
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  results[idx] = fut.result()
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+ print("DONE")
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  except RateLimitError:
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  # You could set results[idx] = None or a default string
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  results[idx] = f"<failed after retries>"
 
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  scores = np.array([p["score"] for p in ranked], dtype=float)
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  # Max normalization
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  max_score = scores.max() if len(scores) > 0 else 1.0
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+ # max_score = 1
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  if max_score == 0:
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  norm_scores = np.zeros_like(scores)
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  else:
 
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  for p, norm in filtered
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  ]
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+ # docs.reverse()
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  return docs
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